Analysis of Surface Thermal Behavior in Different Local Climate

Zones (LCZ): A Case Study in Bragança (Portugal) (2013-2024)

Cátia Rodrigues de Almeida

1,2,3 a

, Artur Gonçalves

and Ana Cláudia Teodoro

1,2 c

Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto,

Rua Campo Alegre, 687, 4169-007 Porto, Portugal

Earth Sciences Institute (ICT), Pole of the FCUP, University of Porto, 4169-007 Porto, Portugal

CIMO, LA SusTEC, Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300 253 Bragança, Portugal

Keywords: Urban Heat Island (UHI), Surface Urban Heat Island (SUHI), Remote Sensing (RS), Albedo, Land Use and

Land Cover (LULC), Land Surface Temperature (LST).

Abstract: The Urban Heat Island (UHI) effect occurs when temperatures in urban areas are higher than surrounding

vegetated areas, especially during the sunset and sunrise. UHI impacts include effects on public health and

well-being, changes to the local microclimate, and influence on the local biome. This study evaluates the Land

Surface Temperature (LST) and the corresponding Surface Urban Heat Island Intensity (SUHIint) across

different Local Climate Zones (LCZs) in Bragança (Portugal) from 2013 to 2024, using images from Landsat

8 and 9 data collected with a portable thermal camera on different surfaces to assess thermal behavior across

different scales. The results confirm the existence of the UHI effect in Bragança, where vegetated areas exhibit

milder temperatures compared to built areas, especially in summer afternoons. Satellite-derived LST data

indicate that the lowest temperature was recorded in an LCZ with vegetation, reaching (-7ºC), while the

highest minimum temperature was observed in an LCZ with higher density of anthropogenic elements (-3ºC).

Thermal camera measurements showed that surfaces such as asphalt and exposed soil reached 80 ºC in the

morning and remained above 60ºC in the afternoon. These findings underscore the importance of considering

mitigation measures, such as increasing vegetation in urbanized areas or replacing impervious surfaces.

1 INTRODUCTION

The viability of human life in each location requires

a series of adaptations in Land Use and Land Cover

(LULC), presenting a contemporary challenge. With

the global population expected to increase from 7.7

billion to 9.7 billion by 2050 (ONU, 2019), it is

essential to pay attention to the surface materials

incorporated in the construction of houses, railways,

commerce, industry, etc. (Oke, 1987; Weng, 2003).

Urbanization can contribute to the formation of the

Urban Heat Island (UHI) effect, characterized by

higher temperatures in urbanized areas compared to

surrounding rural areas (Imhoff et al., 2010; Oke,

1982). A specific approach to this phenomenon is the

Surface Urban Heat Island (SUHI), which assesses

temperature differences based on temporal variability

https://orcid.org/0000-0003-4455-9407

https://orcid.org/0000-0002-4825-6692

https://orcid.org/0000-0002-8043-6431

and surface characteristics (Voogt & Oke, 2003;

Weng & Fu, 2014).

SUHI occurs mainly due to the characteristics of

anthropogenic elements in the urban landscape, such

as asphalt, sidewalks, and streets (Oke, 1988). These

materials have a low albedo, meaning they reflect less

solar radiation and retain more heat (Coakley, 2003).

Figure 1 shows some materials found in an urban

context, along with their respective albedo rates. As

observed, most anthropogenic materials exhibit low

reflectance.

Beyond albedo, the Local Climate Zones (LCZ)

methodology was developed to classify urban and

rural environments based on the local landscape and

physical and thermal characteristics, such as land

cover, terrain structure, building density, surface

materials. LCZs are divided into various categories:

Rodrigues de Almeida, C., Gonçalves, A. and Teodoro, A. C.

Analysis of Surface Thermal Behavior in Different Local Climate Zones (LCZ): A Case Study in Bragança (Portugal) (2013-2024).

DOI: 10.5220/0013282700003935

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 11th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2025), pages 157-164

ISBN: 978-989-758-741-2; ISSN: 2184-500X

157

on one hand, built zones (urban), including blocks of

buildings and residential areas with little or no

vegetation, and on the other, natural zones (rural),

such as forests, parks, water surfaces, etc. (Errea et

al., 2023; Stewart & Oke, 2012).

Figure 1: The albedo of urban materials (adapted from

(EPA - United States Environmental Protection Agency,

n.d.)).

Remote Sensing (RS) is a methodology applied to

understand UHI using data collected by thermal

sensors placed on various platforms. The sensors

record the electromagnetic radiation emitted or

reflected by surfaces without physical contact with

the sampled surface, enabling the calculation of Land

Surface Temperature (LST) (Pachêco, 2001; Weng et

al., 2004). The satellite mission most commonly used

for LST calculation, with freely available data

distribution, is Landsat, which has included a thermal

sensor since Landsat 4 (operational from July 1982 to

June 2001) to Landsat 9 (launched in September

2021) (Almeida et al., 2021; US Geological Survey,

n.d.-a; Zhou et al., 2019). In addition to satellite data,

portable thermal cameras with in situ data collection

can complement the methodology, allowing for a

more detailed analysis of surface reflectance.

The objective of this study is to evaluate the UHI

effect in Bragança (Portugal) using LST data from

Landsat 8 and 9 between 2013 and 2023, along with

LST data from a portable thermal camera collected

during five field campaigns between 2023 and 2024,

divided into summer and winter, to assess the

seasonal influence on the results.

The choice of Bragança is based on: i) its

mountainous setting, which naturally influences wind

circulation and speed, potentially contributing to UHI

formation; ii) centralized urban densification with

surrounding vegetated areas; iii) its classification into

seven distinct LCZs, facilitating the understanding of

its LULC; and iv) as a supplement to the existing

literature, as Bragança's UHI has already been

analyzed using RS images and compared with Air

Temperature (Ta) data from a monitoring network

with 23 weather stations distributed across seven

LCZs between June 29, 2013, and July 5, 2021. In

summer, the correlations were strong and very strong,

while in winter, they were very strong in all sensors,

confirming similar thermal behavior in both

parameters (Almeida et al., 2022, 2023; Menezes,

2017).

Our hypothesis is that the integration of RS data

acquired by satellites and thermal cameras can

confirm the identification of the UHI effect in

Bragança and inspire other researchers to implement

similar approaches, contributing to the advancement

of knowledge and promoting discussions on the need

for sustainable urban planning, including the adoption

of high albedo materials and green infrastructure to

mitigate the UHI effect (Nations, n.d.). The LST

measured by satellites and thermal cameras differs in

specificity, with expected errors due to spatial

resolution and ground proximity. Landsat 8 and 9

sensors acquire LST at 100m, resampled to 30m,

limiting the detection of small transition areas.

Additionally, the altitude difference reduces

atmospheric influences on ground-level data, as

thermal cameras capture at 1.5m, while satellites

operate from 705 km.

2 METHODOLOGY

2.1 Study Area

Located in the northeastern of Portugal (41º48'20" N,

6º45'42" W), the municipality of Bragança has an area

of approximately 1,174 km² and, according to the last

census conducted in 2021, has a population of 35,341

inhabitants (Bragança, n.d.; INE, n.d.) (Figure 2). It is

a region with complex terrain, significant variability

in elevation, and heterogeneous LULC, featuring

urban covers, agriculture, forested areas, exposed

soils, and natural and vegetated areas. Its economy

primarily focuses on public sector service companies

(Gonçalves et al., 2018).

Bragança is characterized by a Mediterranean

climate, with dry summers and mild temperatures

(Csb), as defined in the Köppen-Geiger scale

(Barceló & Nunes, 2009). Two scales influence the

climate: i) regional, with the atmospheric circulation

present in transitional zones (between the ocean and

continents); and ii) a more detailed scale, influenced

by terrain and proximity to water bodies (lakes or

rivers). There are two main rivers in and around

Bragança: Fervença (which crosses the region) and

Sabor, which is located in a peripheral area and has

GISTAM 2025 - 11th International Conference on Geographical Information Systems Theory, Applications and Management

158

less local influence (Bragança, n.d.; Gonçalves et al.,

2018).

One of the environmental actions implemented in

Bragança is the extensive and continuous collection

of temperature and relative humidity data through

TGP-4500 model sensors, TinyTag, Gemini Data

Loggers, Chichester, UK, installed three meters

above the ground, enclosed in a protective white box,

semi-leveled on light poles, and oriented towards the

South.

Figure 2. Identification of the 23 Sensors used in this study

in Bragança, Portugal: Compact low-rise (CLR) (gray);

Compact midrise (CM) (red); Urban green spaces (GAB)

(light green); Large low-rise (LLR) (orange); Open midrise

(OM) (blue); Rural areas (RCD) (green); Sparsely built

(SB) (purple); (adapted from (Almeida et al., 2022)).

The installation criterion for the sensors

considered the different LCZs identified in Bragança,

ensuring at least three sensors in each class, namely:

i) Compact low-rise (CLR): historical city center,

including buildings with high density, medium-low

height, and presence of embedded bricks/stones

(sensors 4 and 6); ii) Compact midrise (CM): modern

high-density buildings, with paved surfaces and

medium-high height (sensors 3, 7, and 13); iii) Urban

green spaces (GAB): predominantly green, covered

by trees and low vegetation (sensors 2, 8, 9, and 11);

iv) Large low-rise (LLR): industrial and commercial

area, with paved parking lots, low or medium density,

featuring low and high buildings (sensors 5, 17, and

21); v) Open midrise (OM): streets with isolated or

low houses, of medium density (sensors 10, 12, 18,

and 22); vi) Sparsely built (SB): transition between

rural and urban environments, with scattered houses

in forested and agricultural areas (sensors 1, 14, and

15); and vii) Rural areas (RCD): isolated rural spaces

surrounding the city (sensors 16, 19, 20, and 23)

(Almeida et al., 2022; Gonçalves et al., 2018). The

data from these sensors enabled studies on UHI in the

region and surrounding areas, including

methodologies with Remote Sensing (Almeida et al.,

2022, 2023; Gonçalves et al., 2014, 2018).

2.2 Data Used and Processing

Conducted

2.2.1 Satellite Images

We used Google Earth Engine (GEE) for satellite

image processing, a cloud-based platform that

processes data remotely, reducing the need for

powerful computers and significantly minimizing

processing time, which already offers a series of

processed products (Hurni et al., 2017; NASA, n.d.-

a). We utilized the "LANDSAT/LC08/C02/T1_L2"

and "LANDSAT/LC09/C02/T1_L2" collections,

with overpass times around 11 AM in Bragança.

These satellites operate in a nearly polar and sun-

synchronous orbit at a nominal altitude of 705 km,

with an orbital inclination of 98.2°, and revisit the

same location every eight days, as they operate in

alternating orbits, improving temporal resolution for

the study areas (US Geological Survey, n.d.-a).

The thermal sensors record data at 100m, which is

resampled and made available to users at 30m. In

GEE, the collections are provided with

atmospherically corrected surface reflectance data,

and the LST is derived from the Land Surface

Reflectance Code (LaSRC), generated through a

single-channel algorithm developed in collaboration

between the Rochester Institute of Technology (RIT)

and NASA's Jet Propulsion Laboratory (JPL)

(NASA, n.d.-a).

We used the code for processing LST (NASA,

n.d.-b) and to filter the data for our study area between

2013 and 2024, considering only those with cloud

coverage below 20%. Subsequently, we used the

quality bands from each collection (QA_PIXEL) to

remove pixels with clouds and cloud shadows.

We utilized the thermal band (B10) from both

collections to obtain the LST. The spectral range is

from 10.6 to 11.19 µm, and the data are recorded at

100m and resampled to 30m. To convert the LST to

Celsius, we applied Equation (1) (NASA, n.d.-a; US

Geological Survey, n.d.-b).

LST = TIR  f



O−

)

Where LST is the Land Surface Temperature

(°C), TIR is the radiance in the thermal infrared band,

is the scale factor of Landsat 8 and 9 (0.00341802),

O is the offset of Landsat 8 and 9 (149.0) and K is the

conversion constant from K to °C (273.15).

Analysis of Surface Thermal Behavior in Different Local Climate Zones (LCZ): A Case Study in Bragança (Portugal) (2013-2024)

159

To evaluate the LST by LCZs, we used a .shp file

of points in GEE corresponding to the locations of the

23 sensors installed in Bragança to extract the LST

values for each of them, and we added a function to

export the data to a .csv file. Between 2013 and 2014,

we identified 158 images, with the first date being

06/29/2013 and the last being 09/15/2024.

Subsequently, we selected results where images

provided data for all 23 sensors, resulting in 123

images spanning all seasons. We further filtered the

data for summer and winter, obtaining 46 images for

summer and 15 for winter, which were used in the

processing. The lower number of winter images can

be attributed to meteorological conditions, with

higher cloud cover and precipitation during winter,

aligning with the Köppen-Geiger classification

(Barceló & Nunes, 2009).

To process and analyze the results, we calculated

the SUHI by applying Equation (2), a methodology

already used in other UHI studies (Almeida et al.,

2022; Menezes, 2017).

𝑆𝑈𝐻𝐼



=𝐿𝑆𝑇



− 𝐿𝑆𝑇





(2)

Where SUHI

Int

is the intensity value of the UHI at

each point; 𝐿𝑆𝑇



is the LST value extracted from

the sensor points; and 𝐿𝑆𝑇





refers to the average of

the LST values extracted the RCD sensors (16, 19, 20,

and 23).

To present the results, we used SPSS software to

create boxplots of the SUHI

int

results, separated by

LCZ and by seasonality (summer and winter), a

methodology already applied in other studies

(Almeida et al., 2022; Saher et al., 2021).

2.2.2 LST from Portable Thermal Camera

For in situ LST imaging, we used the HT Instruments

THT33 portable thermal camera, which has a

resolution of 80x80 pixels, a Field of View (FOV) of

21°x21°, a spectral range of 8 to 14 µm, and an

Instantaneous Field of View (IFOV) (@1m) of 4.53

mrad. Images were taken at a standard height of 1.5m

between the camera and the target to ensure

consistent operator-associated error across all

samples.

The collections were conducted only on clear,

cloud-free days, as these conditions affect

measurement accuracy. Consequently, three data

collection sessions were conducted: two in winter

(02/17/2023 and 02/20/2024) and one in summer

(07/27/2023). Since morning data collections had to

coincide with satellite overpass times, covering all 23

points with T

sensors was not feasible. Instead, ten

representative points were selected to capture spatial

diversity and ensure at least one representative for

each of the seven LCZs. The selected sensors, their

respective LCZs, and the number assigned to each

point for identification in the in situ collection are: i)

CM: sensor 3, point P08; ii) CLR: sensor 6, point P07;

iii) GAB: sensor 9, point P05; iv) SB: sensors 14 and

15, points P09 and P04; v) LLR: sensors 5 and 17,

points P06 and P03; vi) OM: sensors 18 and 22, points

P02 and P10; and vii) RCD: sensor 23, Point P01.

We assessed the diversity in LULC composition

for each selected point and sampled different surfaces

within a 10m radius of each sensor. We conducted

two measurements at each point: i) between 10 am

and 12 pm (within one hour before and after the

satellite’s overpass time in Bragança); and ii) between

1 pm and 3 pm to analyze temperature variability of

the sampled surfaces compared to the first session.

For each sampled surface, we took a photograph

using a Canon EOS 800D camera to classify it

afterward into the following categories: Vegetation;

Exposed soil; Road with stones; Sidewalk; Asphalt;

Sidewalk with shade; Asphalt with shade;

Cobblestone; Iron; Curb and Vegetation with shade.

Each surface is assigned a letter to differentiate its

LULC for the analysis. For example, five different

surfaces were collected at P01, labeled from P01A to

P01E.

The HTMercury33 application was used to

record, visualize, and export the LST results in .pdf

format (User Manual - HTMercury33, n.d.) We

created an R routine to extract specific data from the

.pdf files to Excel, specifically: S (temperature

associated with the fixed central cursor); H

(temperature of the hottest point in the image); C

(temperature of the coldest point in the image); and

Mean (average temperature). Subsequently, we

categorized the data by season (summer and winter)

and generated boxplots with the maximum and

minimum LST.

3 RESULTS AND DISCUSSION

3.1

SUHI

int

According to Figure 3, it can be observed that the

summer data exhibit greater amplitude and more

outliers compared to winter data, which may be

associated with a higher availability of solar radiation

during this season.

In the summer, most sensors showed negative

medians of SUHI

int

, except for sensors 3 (CM), 17

(LLR), and 12 (OM). In these three cases, the

surroundings allowed the entry of short-wave

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radiation in the early hours of the day from the east

(sunrise), warming the surface in the early morning.

Additionally, the structures of the OM (12) and CM

(3) sensors, which contain medium-height buildings,

may interfere with heat exchange with the

environment due to shadow projection, making the

area less prone to heat accumulation (Zheng et al.,

2019). In the case of LLR (17), due to the presence of

heterogeneous profiles, including industrial and

commercial areas and paved parking lots, heat can be

absorbed, especially by elements with low albedo

(pavements, structures made of cement, etc.) (Zheng

et al., 2019). Still in summer, the three sensors that

exhibited the lowest medians consist of vegetation

elements, as in the case of sensors 9 and 11 (GAB)

and 15 (SB), reinforcing that the presence of

vegetation aids in regulating SUHI

int

(Dai et al.,

2019).

Figure 3. Boxplot illustrating the Surface Urban Heat Island

Intensity (SUHI

int

) across various Local Climate Zones

(LCZ). The zones are categorized as follows: i) Compact

Low-Rise (CLR) in gray; ii) Compact Midrise (CM) in red;

iii) Urban Green Spaces (GAB) in green; iv) Large Low-

Rise (LLR) in orange; v) Open Midrise (OM) in light blue;

and vi) Sparsely Built (SB) in purple.

In summer, regarding higher temperatures and

disregarding outliers, sensors 17 (LLR), 12, and 18

(OM) exhibited the highest values, reinforcing that

the presence of anthropogenic elements may

contribute to heat accumulation on the analyzed

surfaces (Oke, 1987, 1988; Voogt & Oke, 2003).

Finally, the lowest temperature is associated with

sensor 9 (GAB), reaffirming the importance of

preserving and prioritizing vegetation in spaces to

regulate heat (Miles & Esau, 2020).

In winter, the medians were mainly below 0,

except for sensors 13 (CM), 2 (GAB), 17 (LLR), and

12 (OM). Most of these sensors are integrated into

LCZs with the presence of anthropogenic materials,

converging with the values identified in summer,

except for sensor 2, which contains vegetation. This

behavior can be explained by the season: in winter,

vegetation enters a state of dormancy to protect itself

from cooler temperatures, reducing its metabolic

activities and, consequently, slowing down heat

exchanges. This also limits photosynthesis, as it does

in other seasons under similar conditions (Du et al.,

2016; French & Inamdar, 2010). There is also leaf

shedding in deciduous trees, known as senescence,

which may also contribute to this result (Mariën et al.,

2019).

As in summer, the highest temperatures are

associated with sensors containing anthropogenic

elements, as seen in sensors 17 (LLR), 4 and 6 (CLR),

and 3 (CM). In contrast, the lowest temperatures

occurred in sensors 9 and 11 (GAB), which contain

vegetation (Tang et al., 2017).

3.2 LST from Thermal Camera

The number of data points available in each

measurement session was 220 images (110 in the

morning and 110 in the afternoon) on 02/17/2023,

224 on 02/20/2024 (112 in each period), and 186 on

07/27/2023 (93 per period). The differences in sample

size are attributed to occasional measurement errors

related to the equipment. Figure 4 shows the recorded

LST values in summer across different surfaces, with

surface identification highlighted in the legend.

In general, summer temperatures are much higher

than winter temperatures due to increased

electromagnetic energy availability and solar angle,

which minimizes shadow projection. Some surfaces

reached nearly 80 ºC in the morning and above 60 ºC

in the afternoon, mainly asphalt and exposed soil. In

summer, vegetation also retains greater heat in the

morning and has cooler temperatures in the afternoon.

This can be explained by vegetation's prolonged

exposure to electromagnetic energy in the early hours

compared to built-up areas, which are shaded by

structures and accumulate less heat. In the afternoon,

the opposite is observed: vegetation temperatures

tend to be cooler due to latent heat exchange with the

environment, especially through evapotranspiration,

which reduces the heating of these surfaces, in

addition to the shading effect provided by tree

canopies. In urban areas with low albedo surfaces,

such as asphalt and concrete, the radiative balance

favors the absorption of short-wavelength radiation,

resulting in the surface heating of these areas (Oke,

1988; Yu et al., 2019).

In a more detailed LCZ analysis, at P07 (CLR), we

observe that the LULC materials such as asphalt and

elements of iron, along with the lack of vegetation,

contribute

to higher temperatures in summer,

Analysis of Surface Thermal Behavior in Different Local Climate Zones (LCZ): A Case Study in Bragança (Portugal) (2013-2024)

161

Figure 4. Boxplot of Land Surface Temperature (LST) at the ten in situ measurement points using a thermal camera.

particularly in the afternoon, due to the thermal inertia

of these materials, which accumulate heat throughout

the day. In winter, temperatures are slightly higher

than most vegetation classes, especially in the

afternoon (Cai et al., 2017). At P08 (CM), asphalt and

sidewalk temperatures were higher in summer than in

winter, both in the morning and afternoon, which may

be associated with the angle of electromagnetic

energy. In summer, this energy is more direct,

reducing shading from existing buildings in the area

(Yu et al., 2019).

At P05 (GAB), it is evident that vegetation created

a cooler microclimate, with lower temperatures than

densely built areas, especially in summer at both times

of the day. The LST values for these locations are

slightly lower in winter than in built-up areas,

although they represent a less pronounced cooling.

This could be associated with the lower radiation

levels and the vegetation’s dormancy process, which

reduces evapotranspiration and impacts natural

cooling, as discussed in the UHI

int

results (Rose,

2019).

At points P03 and P06 (LLR), both in summer and

winter, the LST showed higher values in paved areas,

similar to CM and CLR points, which may be due to

the lack of shading in the area. Regarding points P02

and P10 (OM), LST values in summer were more

moderate, with warmer asphalt areas and cooler

vegetated areas, particularly in the afternoon,

potentially helping to regulate local temperature. In

winter, due to lower construction density and

vegetation presence, temperatures were milder at

these points (Oke et al., 2017).

For points P04 and P09 (SB), in both summer and

winter, vegetation showed higher values in the

morning, possibly due to heat accumulation in the

early hours of the day, a similar effect to that observed

at point P01 (RCD). The absence of large vertical

barriers in open areas facilitates heat exchange with

the environment in the night, promoting heat

dissipation through convection. However, surface

characteristics and solar radiation absorption still

significantly influence daytime temperature retention

in these areas. For this reason, it is suggested that

UHI studies should be conducted after sunset,

incorporating additional methodologies such as the

use of Unmanned Aerial Vehicle (UAV), which

provide greater flexibility regarding the timing of data

collection.

Despite scale differences, the results were similar,

with higher surface temperatures in built-up areas,

reinforcing the need for mitigation measures.

Incorporating vegetated surfaces in densely

urbanized areas, such as green roofs and substituting

impermeable surfaces with alternatives like

permeable asphalt, is essential (Cai et al., 2017;

Gonçalves et al., 2014; Mullerova & Williams, 2019).

It is worth noting that, especially in summer,

additional data through field visits are necessary to

verify if these initial thermal behavior results are

consistent across time.

4 CONCLUSIONS

This study used RS data to assess surface thermal

behavior across different LCZs and identify the UHI

GISTAM 2025 - 11th International Conference on Geographical Information Systems Theory, Applications and Management

162

effect in Bragança. We utilized both satellite data and

in situ data collected from a thermal camera,

comparing the results from both techniques. In terms

of spatial resolution and measurement height, the two

methods differ: while Landsat is efficient for mapping

larger, more homogeneous areas, the use of the

thermal camera provides more detailed, localized

surface data, being less influenced by atmospheric

parameters and more effective at mapping

heterogeneous areas. This makes the integration of

these techniques particularly valuable for UHI studies.

The SUHI

int

, calculated from satellite data, was

higher in summer, especially in LCZs with

anthropogenic surfaces, compared to those with

vegetation, highlighting the influence of seasonality.

In situ measurements corroborated these results,

showing that impermeable surfaces retained more

heat, particularly during summer afternoons.

The analyses suggest that integrating green areas

and using high-albedo materials are strategies to

mitigate the UHI effect and promote climate resilient

cities. Future research could explore long-term UHI

dynamics, with additional field data collection and

the development of specific mitigation strategies

tailored to the different LCZs in Bragança, whose

findings could be applied to other urban contexts.

ACKNOWLEDGEMENTS

This work was supported by national funds through

FCT/MCTES (PIDDAC): CIMO, UIDB/00690/2020

(DOI: 10.54499/UIDB/00690/2020) and UIDP/

00690/2020 (DOI: 10.54499/UIDP/00690/2020); and

SusTEC, LA/P/0007/2020 (DOI: 10.54499/LA/

P/0007/2020). The authors are grateful to the

Foundation for Science and Technology, I.P., projects

UIDB/04683/2020 (https://doi.org/10.54499/UIDB/

04683/2020), IDP/04683/2020 (https://doi.org/

10.54499/ UIDP/04683/2020. Cátia Rodrigues de

Almeida was financially supported by Portuguese

national funds through FCT (Grant:

PRT/BD/153518/2021).

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